How to determine the top percentile of a range

[Ziplock]

I am trying to build a Monte Carlo system for my sports league where I can determine the percentage each team will finish in the top 8 of the league after the last game of the season. Each team has an equal chance of beating their opponents every week and the win will get added to that team's record. After the last week (I have each week in a frame), I need to give a +1 point to an alterable variable for every team that has finished in the top 8 for that 1 simulation. Then I'll repeat this thousands of times to get a good average.

Is there a good way of saying "Check this variable against all of these other variables. If it's in the top percentile, add 1 to another variable?"

[schrodinger]

Hey Ziplock,
trying to put my mind on this but can't understand what you're up to

you want to check a value against a set of other values,
and do something if it's in the top percentile of the series (pre-calculated, i guess?)

[Ziplock]

OK, I have 12 teams in my league, but only 8 make the playoffs. I run random results for the regular season and end up with the following results:

Team 1: 23 points
Team 2: 27 points
Team 3: 43 points
Team 4: 19 points
Team 5: 31 points
Team 6: 55 points
Team 7: 21 points
Team 8: 12 points
Team 9: 18 points
Team 10: 35 points
Team 11: 25 points
Team 12: 39 points

How do I pick out the 8 teams that have the most points so I can add to their probability of making the playoffs?

[schrodinger]

I think the easier-quicker path would be adding those scores as "trailed" values to a list object
and adding team ID to the "line data" for that row

i.e.

add
line 1 : 023 (line data= 1 for team 1)
line 2 : 027 (line data= 2 for team 2)
...

(assuming a range from 000 to 999)
you can add trailing zeroes with string functions (something like left$(000,3-len("score")) + "score")

then simply use the list "automatic sort" option,
and pick last 8 rows "line data" value to retrieve team IDs
(or store in the list 999-"team score" and pick first 8 rows)